On Wed, Jun 17, 2015 at 10:53 PM, Jens Jørgen Mortensen <je...@fysik.dtu.dk> wrote: > >>> type(n) > <type 'numpy.int64'> > >>> isinstance(n, int) > True > > With Python 3.4 you get False. I think I understand why (np.int64 is no > longer a subclass of int).
Yep, that's correct. > So, I did this instead: > > import numbers > isinstance(n, numbers.Integral) > > which works fine (with numpy-1.9). Is this the "correct" way or is > there a better way to do it? That's the correct way to check whether an arbitrary object is of some integer-like-type, yes :-). There are alternatives, and there's some argument that in Python, doing explicit type checks like this is usually a sign that one is doing something awkward, but that's a more general issue that it's hard to comment on here without more detail about what exactly you're trying to accomplish. > I would imagine that a lot of code will > break because of this - so it would be nice if isinstance(n, int) could > be made to work the same way in 2 and 3, but I don't know if this is > possible (or desirable). It's not possible, unfortunately. In py2, 'int' is a 32- or 64-bit integer type, so we can arrange for numpy's int32 or int64 objects to be laid out the same in memory, so everything in python that expects an int (including C API functions) can handle a numpy int. In py3, 'int' is an arbitrary width integer bignum, like py2 'long', which is fundamentally different from int32 and int64 in both semantics and implementation. -n -- Nathaniel J. Smith -- http://vorpus.org _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
